1. Field of the Invention
The present invention is directed to computer systems. More particularly, it is directed to image processing.
2. Description of the Related Art
Image processing often involves the application of various filtering operations on image data using one or more convolution operations. For example, a median filter looks at pixel values (e.g., values indicating the color and/or intensity of the pixels in an image) within a box-shaped region around each pixel and selects the median value for the resulting image (i.e., the filtered output image.) In another example, a bilateral box filter replaces the value at a pixel, (i, j, k), with a weighted sum of the pixel values in the neighborhood of the pixel. Each weight is the product of a factor dependent on the distance (in pixels) to (i, j, k) and another factor dependent on the difference in intensity between the pixels and the pixel at (i, j, k).
These and other filters are typically implemented using a series of convolution steps performed across the rows and columns of an image. These convolutions may involve large numbers of operations on subsets of the input image data, and are often implemented by applying a kernel of support over the image in a sliding window. Different techniques for speeding up convolutions on image data have been developed, including the use of histograms. A histogram may be expressed as an array of buckets whose values are dependent on the values of pixels in a given region of an image. Once computed, the histogram may be used to accelerate median filtering, bilateral filtering, and other filtering functions. For example, a histogram whose values represent counts of pixel values may be searched to find the pixel value greater than half the population (i.e., the median value). However, histograms can be expensive to generate, maintain, and operate on, especially for images and/or kernels containing a large number of pixels.